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Machine learning algorithms have evolved from knowledge-driven expert systems to data-driven systems enabled by machine learning. Hundreds of machine learning methods now exist, including various types of neural networks, genetic algorithms, decision trees, and more. Automated machine learning (AutoML) is now possible due to advancements in expert systems, computing power, data manipulation techniques, and sophisticated learning algorithms. AutoML involves automated data cleansing, transformations, model training and evaluation, and can deploy models for use in various applications.
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- 1. Automated Machine Learning (AutoML) Putting AI to Work in Data Science Gene Ferruzza Sr. Manager Data Science – Valassis Digital PyData Triangle
- 2. The Evolution of Machine Learning Machine Learning Algorithms Drive Data-Driven AI Systems Statistics Neural Networks Expert Systems Genetic Algorithms AI Source: 1990 International Joint Conference on Neural Networks (IJCNN) • In the 1980’s the focus in AI transitioned from Knowledge- Driven Systems to Data-Driven Systems enabled through Machine Learning • Decision Trees emerged using ID3 and CART statistical classifying algorithms for rule induction methods • Multiple Neural Network paradigms and Genetic Algorithms proved to be successful at learning from data
- 3. The Evolution of Machine Learning Statistics • Linear Regression • Logistic Regression • Non-Linear Least Squares • Many Other Math Based Methods Neural Networks • BackProp • SOM,CNN • Deep Learning • Visual Recognition • Speech Recognition Rule-Based Decisions • Decision Trees • Rule Induction • Expert Systems • Rule Strategies Genetic Algorithms • Model Evolution (crossovers & mutations) • Directed-Random Search • Hyperparamter Configuration • Fitness Function Artificial Intelligence • Decision Processing • Adaptive Learning • Digital/Mechanical Integration • Feature Engineering • NLP • Bayesian Networks • K-Network • SVM Network • XGBoost • Random Forest • Boosted Trees • ID3, C4.5 • Chaid, CART • Model Seeding • Decision Tree Induction • Search Techniques • Genetic Representation • Feature Selection • Model Seeding • Today AI is pervasive and data- driven, raising the importance of machine learning • 100’s of machine learning methods exist and continue to drive AI capabilities • Expert Systems still play a role but not always a primary role for data- driven processes.
- 4. The Evolution of Machine Learning EXPECTATIONS TIME Innovation Triggers Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity 20102000’s1990’s1980’s Desktop Computing Recurrent NN (Hopfield) SAS Desktop Digital Insurgence (IoT) Intelligent Agents Reinforcement Learning Data Siloes Poor Data Access Inadequate Compute Power Back Propagation Deep Learning Deep Blue – Chess Champ Watson Wins Jeopardy Facial Recognition Natural Language Processing Knowledge-Driven to Data-Driven Speech Recognition Neocognitron (CNN) GPUs Random Forest Deep Face AlexNet (CNN) ImageNet AI Expert Systems (Knowledge Driven) ID3 – Tree Algorithm NetTalk Fall of Expert Systems High Cost of Expert Systems Loss of Major AI Funding Expert System Popularity Revival of Connectionism ML Solutions Incognito Collapse of Lisp Machines AutoML is Made Possible through Expert Systems, Compute Power, Data Manipulation and Advanced Learning Algorithms AI WINTER
- 5. An AutoML Process Walk Through Cleansing/ Analysis Data Transformations Exploratory Data Analysis Machine Learning Model Evaluation Model Deployment • Automated Field Analysis. Field Statistics and Distributions • Data Input Using .csv, .txt or .xlsx Files Only • Missing Data Imputation • Partitions Data Into Train/Test Datasets SOLVING FOR SINGLE MISSING FIELDS
- 6. An AutoML Process Walk Through Cleansing/ Analysis Data Transformations Exploratory Data Analysis Machine Learning Model Evaluation Model Deployment • Automated Field Analysis. Field Statistics and Distributions • Data Input Using .csv, .txt or .xlsx Files • Modeled Missing Data Imputation • Partitions Data Into Train/Test Datasets
- 7. An AutoML Process Walk Through Cleansing/ Analysis Data Transformations Exploratory Data Analysis Machine Learning Model Evaluation Model Deployment • Automated Data Representations & Entire Set of Transformations • Genetic Algorithm Drives Feature Selection Sets
- 8. An AutoML Process Walk Through Cleansing/ Analysis Data Transformations Exploratory Data Analysis Machine Learning Model Evaluation Model Deployment Neural Network (Single Layer) Neural Network (Deep Learning) Linear Model (Regression) Neural Network (Multi-Class & SOM)
- 9. An AutoML Process Walk Through Cleansing/ Analysis Data Transformations Exploratory Data Analysis Machine Learning Model Evaluation Model Deployment Primary Process Builds hundreds of models using pre- identified feature sets using Cascade Learning and default hyper- parameters Secondary Process • Runs the Primary Process with a GA driving hyper- parameters interations • Store top 100 performing models and uses a GA for final ensemble testing
- 10. Model Deployment Options Embedded Application Python, “C” Flashcode Model Deployed in MSExcel An AutoML Process Walk Through Cleansing/ Analysis Data Transformations Exploratory Data Analysis Machine Learning Model Evaluation Model Deployment Model Deployed Through a Web Service